Model Predictive Path Integral Docking of Fully Actuated Surface Vessel

TL;DR

This paper introduces a novel autonomous docking method for surface vessels that combines Model Predictive Path Integral control with real-time LiDAR detection, optimizing safety, precision, and efficiency in confined marine environments.

Contribution

The paper presents a unique integration of MPPI control with real-time LiDAR-based detection for improved autonomous vessel docking, including an adaptive detection pipeline and extensive simulation validation.

Findings

Successful docking from various initial positions

Maintains safe clearances and smooth motion

Validated in realistic simulation environment

Abstract

Autonomous docking remains one of the most challenging maneuvers in marine robotics, requiring precise control and robust perception in confined spaces. This paper presents a novel approach integrating Model Predictive Path Integral(MPPI) control with real-time LiDAR-based dock detection for autonomous surface vessel docking. Our framework uniquely combines probabilistic trajectory optimization with a multiobjective cost function that simultaneously considers docking precision, safety constraints, and motion efficiency. The MPPI controller generates optimal trajectories by intelligently sampling control sequences and evaluating their costs based on dynamic clearance requirements, orientation alignment, and target position objectives. We introduce an adaptive dock detection pipeline that processes LiDAR point clouds to extract critical geometric features, enabling real-time updates of docking parameters. The proposed method is extensively validated in a physics-based simulation environment that incorporates realistic sensor noise, vessel dynamics, and environmental constraints. Results demonstrate successful docking from various initial positions while maintaining safe clearances and smooth motion characteristics.